Machine Learning

MDGEN: Generative modeling unlocks MD data for diverse tasks, achieving significant speedups via flexible multi-task surrogate models.

Generative Fractional Diffusion Models (GFDM) leverages fractional diffusion processes for superior image generation, enhancing diversity and quality while addressing existing diffusion model limitati…

Generative Forests (GFs) revolutionize tabular data generation with a novel forest-based model and a simple boosting algorithm offering strong convergence guarantees, significantly outperforming curre…

Generative adversarial model-based optimization via adaptive source critic regularization (aSCR) significantly boosts offline optimization accuracy.

LLMs guided by Monte Carlo Tree Search generate precise, efficient Python code as world models for model-based reinforcement learning, significantly improving sample efficiency and inference speed.

Researchers developed FSAFW, a novel framework generating universal adversarial perturbations effective against various Few-Shot Learning paradigms, surpassing baseline methods by over 16%.

CP3ER, a novel consistency policy with prioritized proximal experience regularization, significantly boosts sample efficiency and stability in visual reinforcement learning, achieving state-of-the-art…

QGCNs generalize CNNs to graph data via learnable neighborhood quantization, achieving state-of-the-art performance on graph datasets.

This paper proposes Functional t-SVD and ERM-DS to solve multi-output regression under Combinatorial Distribution Shift (CDS), providing robust performance guarantees.

This paper presents a novel method for fast and exact conformalization, leveraging inherent piecewise smoothness to dramatically accelerate uncertainty quantification in machine learning models.

Novel imputation method, scCR, leverages complete gene-gene relationships (associating & dissociating) for superior single-cell RNA sequencing data recovery, significantly outperforming current state-…

GDeR: A novel dynamic graph pruning method boosts GNN training efficiency and robustness by intelligently selecting a representative subset of training data, mitigating issues caused by imbalanced or …

GP-TopK: A novel contextual bandit algorithm uses Gaussian processes with a Kendall kernel for efficient & accurate top-k recommendations, even with limited feedback.

This paper delivers non-asymptotic accuracy bounds for confidence intervals in linear stochastic approximation, leveraging a novel multiplier bootstrap method.

GIN, a novel approximate Bayesian inference algorithm, efficiently handles nonlinear state-space models with high-dimensional, noisy observations by disentangling observation and dynamics. Achieving l…

GACL: a novel exemplar-free technique for generalized analytic continual learning, achieves superior performance by analytically solving the weight-invariant property for handling real-world data.

Constrained sampling solved! New functional gradient flow method (CFG) efficiently samples from constrained probability distributions via a novel boundary condition for gradient flows, achieving prov…

PepGLAD, a novel generative model, revolutionizes full-atom peptide design by leveraging geometric latent diffusion to significantly enhance peptide diversity and binding affinity.

FUG: A new graph contrastive pre-training strategy solves GNN transferability issues across datasets with diverse node features, achieving comparable performance to retraining while significantly impr…

FSP-LAPLACE efficiently integrates interpretable function-space priors into Bayesian deep learning via a novel Laplace approximation, significantly improving uncertainty estimates and model performanc…